Department of Medical Laboratory, the Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China.
School of Laboratory Medicine, Hubei University of Traditional Chinese Medicine, Wuhan 430065, China.
Biosci Rep. 2020 Feb 28;40(2). doi: 10.1042/BSR20193875.
The post-imaging, mathematical predictive model was established by combining demographic and imaging characteristics with a pulmonary nodule risk score. The prediction model provides directions for the treatment of pulmonary nodules. Many studies have established predictive models for pulmonary nodules in different populations. However, the predictive factors contained in each model were significantly different. We hypothesized that applying different models to local research groups will make a difference in predicting the benign and malignant lung nodules, distinguishing between early and late lung cancers, and between adenocarcinoma and squamous cell carcinoma. In the present study, we compared four widely used and well-known mathematical prediction models.
We performed a retrospective study of 496 patients from January 2017 to October 2019, they were diagnosed with nodules by pathological. We evaluate models' performance by viewing 425 malignant and 71 benign patients' computed tomography results. At the same time, we use the calibration curve and the area under the receiver operating characteristic curve whose abbreviation is AUC to assess one model's predictive performance.
We find that in distinguishing the Benign and the Malignancy, Peking University People's Hospital model possessed excellent performance (AUC = 0.63), as well as differentiating between early and late lung cancers (AUC = 0.67) and identifying lung adenocarcinoma (AUC = 0.61). While in the identification of lung squamous cell carcinoma, the Veterans Affairs model performed the best (AUC = 0.69).
Geographic disparities are an extremely important influence factors, and which clinical features contained in the mathematical prediction model are the key to affect the precision and accuracy.
通过将人口统计学和影像学特征与肺结节风险评分相结合，建立影像学后数学预测模型。该预测模型为肺结节的治疗提供了方向。许多研究已经在不同人群中建立了肺结节预测模型。然而，每个模型中包含的预测因素有很大的不同。我们假设将不同的模型应用于当地研究组，将对预测良性和恶性肺结节、区分早期和晚期肺癌以及腺癌和鳞状细胞癌产生影响。在本研究中，我们比较了四个广泛使用且知名的数学预测模型。
我们对 2017 年 1 月至 2019 年 10 月期间的 496 名经病理诊断为结节的患者进行了回顾性研究。我们通过观察 425 例恶性和 71 例良性患者的计算机断层扫描结果来评估模型的性能。同时，我们使用校准曲线和接收者操作特征曲线下的面积（缩写为 AUC）来评估一个模型的预测性能。
我们发现，在区分良恶性方面，北京大学人民医院模型表现出色（AUC=0.63），在区分早期和晚期肺癌方面（AUC=0.67）以及识别肺腺癌方面（AUC=0.61）表现出色。而在识别肺鳞状细胞癌方面，退伍军人事务模型表现最佳（AUC=0.69）。
地理位置差异是一个极其重要的影响因素，数学预测模型中包含哪些临床特征是影响精度和准确性的关键。
